• Title/Summary/Keyword: 작업자 탐지

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A Study on Updating the Knowledge Structure Using New Topic Detection Methods (새로운 주제 탐지를 통한 지식 구조 갱신에 관한 연구)

  • Kim, Pan-Jun;Chung, Young-Mee
    • Journal of the Korean Society for information Management
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    • v.22 no.1 s.55
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    • pp.191-208
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    • 2005
  • This study utilizes various approaches for new topic detection in the process of assigning and updating descriptors, which is a representation method of the knowledge structure. Particularly in the case of occurring changes on the knowledge structure due to the appearance and development of new topics in specific study areas, new topic detection can be applied to solving the impossibility or limitation of the existing index terms in representing subject concepts. This study confirms that the majority of newly developing topics in information science are closely associated with each other and are simultaneously in the phase of growth and development. Also, this study shows the possibility that the use of candidate descriptor lists generated by new topic detection methods can be an effective tool in assisting indexers. In particular. the provision of candidate descriptor lists to help assignment of appropriate descriptors will contribute to the improvement of the effectiveness and accuracy of indexing.

Development of real-time defect detection technology for water distribution and sewerage networks (시나리오 기반 상·하수도 관로의 실시간 결함검출 기술 개발)

  • Park, Dong, Chae;Choi, Young Hwan
    • Journal of Korea Water Resources Association
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    • v.55 no.spc1
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    • pp.1177-1185
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    • 2022
  • The water and sewage system is an infrastructure that provides safe and clean water to people. In particular, since the water and sewage pipelines are buried underground, it is very difficult to detect system defects. For this reason, the diagnosis of pipelines is limited to post-defect detection, such as system diagnosis based on the images taken after taking pictures and videos with cameras and drones inside the pipelines. Therefore, real-time detection technology of pipelines is required. Recently, pipeline diagnosis technology using advanced equipment and artificial intelligence techniques is being developed, but AI-based defect detection technology requires a variety of learning data because the types and numbers of defect data affect the detection performance. Therefore, in this study, various defect scenarios are implemented using 3D printing model to improve the detection performance when detecting defects in pipelines. Afterwards, the collected images are performed to pre-processing such as classification according to the degree of risk and labeling of objects, and real-time defect detection is performed. The proposed technique can provide real-time feedback in the pipeline defect detection process, and it would be minimizing the possibility of missing diagnoses and improve the existing water and sewerage pipe diagnosis processing capability.

The implementation of a Gd-pMOSFET thermal neutron detector and the enhancement of its sensitivity (Gd-pMOSFET 열중성자 측정기 구현 및 감도개선)

  • Lee, Nam-Ho;Kim, Seung-Ho
    • Proceedings of the KIEE Conference
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    • 2005.10b
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    • pp.430-432
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    • 2005
  • 저에너지 중성자가 가톨리늄(Gd) 막에 입사되면 중성자 포획과정에서 전환전자가 생성된다. 이 전환전자에 의해 pMOSFET $SiO_2$ 산화층에서 발생된 전자-전공쌍이 발생되고, 이 가운데 정공은 산화층 내부에 쉽게 붙잡혀(Trap) 양전하 센터로 작용하게 된다. 이 축적된 전하는 pMOSFET의 문턱전압(Threshold voltage)을 변화시킨다. 본 연구에서는 이러한 간접측정 원리를 이용하여 열중성자를 실기간 탐지할 수 있는 반도체형 탐지소자를 개발하고 하나로(HANARO) 방사선장에서의 시험을 통해 성능을 검증하였다. 그리고 감도관련 변수의 최적화를 통하여 작업자가 사용 가능한 범위의 고감도 열중성자 선량계로 개선 제작하였다. 개발된 선량계는 소형으로 실시간 열중성자 측정이 가능하며 감마방사선으로부터 독립적으로 열중성자를 측정할 수 있는 장점도 지니고 있다.

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Extraction of Worker Behavior at Manufacturing Site using Mask R-CNN and Dense-Net (Mask R-CNN과 Dense-Net을 이용한 제조 현장에서의 작업자 행동 추출)

  • Rijayanti, Rita;Hwang, Mintae;Jin, Kyohong
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • 2022.05a
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    • pp.150-153
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    • 2022
  • This paper reports a technique that automatically extracts object shapes through Dense-Net, and subsequently, detects the objects using Mask R-CNN in a manufacturing site, in which workers and objects are mixed. It is based on the customized factory dataset by targeting workers, machines, tools, control boxes, and products as the objects. Mask R-CNN supports multi-object recognition as a well-known object recognition method, while Dense-Net effectively extracts a feature from multiple and overlapping objects. After immediate implementation using the two technologies, the object is naturally extracted from a still image of the manufacturing site to describe image. Afterwards, the result is planned to be used to detect workers' abnormal behavior by adding a label on the objects.

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Development of Object Detection Algorithm Using Laser Sensor for Intelligent Excavation Work (자동화 굴삭기 작업을 위한 레이저 선서의 장애물 탐지 알고리즘 개발)

  • Soh, Ji-Yune;Kim, Min-Woong;Lee, Jun-Bok;Han, Choong-Hee
    • Proceedings of the Korean Institute Of Construction Engineering and Management
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    • 2008.11a
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    • pp.364-367
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    • 2008
  • Earthwork is very equipment-intensive task and researches related to automated excavation have been conducted. There is an issue to secure the safety for an automated excavating system. Therefore, this paper focuses on how to improve safety for semi- or fully-automated backhoe excavation. The primary objective of this research is to develop object detection algorithm for automated safety system in excavation work. In order to satisfy the research objective, a diverse sensing technologies are investigated and analysed in terms of functions, durability, and reliability and verified its performance by several tests. The authors developed the objects detecting algorithm for user interface program using laser sensor. The results of this study would be the basis for developing the automated object detection system.

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A IDS system of Agent Based (에이전트기반의 IDS)

  • 이상훈;송상훈;노용덕
    • Proceedings of the Korean Information Science Society Conference
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    • 2002.04a
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    • pp.898-900
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    • 2002
  • 컴퓨터망의 확대 및 컴퓨터 이용의 증가에 따른 부작용으로 컴퓨터 보안 문제가 중요하게 대두되고 있다. 이에 따라 침입자들로부터 침입을 줄이기 위한 침입탐지 시스템에 관한 연간가 활발히 논의되고 있다. 본 논문에서 IDS 모델들의 소개와 새로운 IDS의 모델을 제시하고 단위 침입 행동별로 학습된 모니터링 프로세서에서 전송되는 사용자 위협 메시지에 대한 처리를 담당하는 조정자 에이전트 시스템을 설계하고자 한다. 본 논문에 제안된 조정자는 안정화된 메시지 처리 문제 뿐 아니라 기존 모델의 에이전트간 협력 작업에 의해 처리되었던 침입판단 기능 및 모니터링 프로세서들의 관리 기능 또한 수행하도록 한다. 그리고 시스템의 유연성 및 확장성 향상을 하도록 하였다.

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Detection of False Data Injection Attacks in Wireless Sensor Networks (무선 센서 네트워크에서 위조 데이터 주입 공격의 탐지)

  • Lee, Hae-Young;Cho, Tae-Ho
    • Journal of the Korea Society for Simulation
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    • v.18 no.3
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    • pp.83-90
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    • 2009
  • Since wireless sensor networks are deployed in open environments, an attacker can physically capture some sensor nodes. Using information of compromised nodes, an attacker can launch false data injection attacks that report nonexistent events. False data can cause false alarms and draining the limited energy resources of the forwarding nodes. In order to detect and discard such false data during the forwarding process, various security solutions have been proposed. But since they are prevention-based solutions that involve additional operations, they would be energy-inefficient if the corresponding attacks are not launched. In this paper, we propose a detection method that can detect false data injection attacks without extra overheads. The proposed method is designed based on the signature of false data injection attacks that has been derived through simulation. The proposed method detects the attacks based on the number of reporting nodes, the correctness of the reports, and the variation in the number of the nodes for each event. We show the proposed method can detect a large portion of attacks through simulation.

Development of a Web Platform System for Worker Protection using EEG Emotion Classification (뇌파 기반 감정 분류를 활용한 작업자 보호를 위한 웹 플랫폼 시스템 개발)

  • Ssang-Hee Seo
    • Journal of Internet of Things and Convergence
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    • v.9 no.6
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    • pp.37-44
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    • 2023
  • As a primary technology of Industry 4.0, human-robot collaboration (HRC) requires additional measures to ensure worker safety. Previous studies on avoiding collisions between collaborative robots and workers mainly detect collisions based on sensors and cameras attached to the robot. This method requires complex algorithms to continuously track robots, people, and objects and has the disadvantage of not being able to respond quickly to changes in the work environment. The present study was conducted to implement a web-based platform that manages collaborative robots by recognizing the emotions of workers - specifically their perception of danger - in the collaborative process. To this end, we developed a web-based application that collects and stores emotion-related brain waves via a wearable device; a deep-learning model that extracts and classifies the characteristics of neutral, positive, and negative emotions; and an Internet-of-things (IoT) interface program that controls motor operation according to classified emotions. We conducted a comparative analysis of our system's performance using a public open dataset and a dataset collected through actual measurement, achieving validation accuracies of 96.8% and 70.7%, respectively.

Solitary Work Detection of Heavy Equipment Using Computer Vision (컴퓨터비전을 활용한 건설현장 중장비의 단독작업 자동 인식 모델 개발)

  • Jeong, Insoo;Kim, Jinwoo;Chi, Seokho;Roh, Myungil;Biggs, Herbert
    • KSCE Journal of Civil and Environmental Engineering Research
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    • v.41 no.4
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    • pp.441-447
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    • 2021
  • Construction sites are complex and dangerous because heavy equipment and workers perform various operations simultaneously within limited working areas. Solitary works of heavy equipment in complex job sites can cause fatal accidents, and thus they should interact with spotters and obtain information about surrounding environments during operations. Recently, many computer vision technologies have been developed to automatically monitor construction equipment and detect their interactions with other resources. However, previous methods did not take into account the interactions between equipment and spotters, which is crucial for identifying solitary works of heavy equipment. To address the drawback, this research develops a computer vision-based solitary work detection model that considers interactive operations between heavy equipment and spotters. To validate the proposed model, the research team performed experiments using image data collected from actual construction sites. The results showed that the model was able to detect workers and equipment with 83.4 % accuracy, classify workers and spotters with 84.2 % accuracy, and analyze the equipment-to-spotter interactions with 95.1 % accuracy. The findings of this study can be used to automate manual operation monitoring of heavy equipment and reduce the time and costs required for on-site safety management.

Effect on self-enhancement of deep-learning inference by repeated training of false detection cases in tunnel accident image detection (터널 내 돌발상황 오탐지 영상의 반복 학습을 통한 딥러닝 추론 성능의 자가 성장 효과)

  • Lee, Kyu Beom;Shin, Hyu Soung
    • Journal of Korean Tunnelling and Underground Space Association
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    • v.21 no.3
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    • pp.419-432
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    • 2019
  • Most of deep learning model training was proceeded by supervised learning, which is to train labeling data composed by inputs and corresponding outputs. Labeling data was directly generated manually, so labeling accuracy of data is relatively high. However, it requires heavy efforts in securing data because of cost and time. Additionally, the main goal of supervised learning is to improve detection performance for 'True Positive' data but not to reduce occurrence of 'False Positive' data. In this paper, the occurrence of unpredictable 'False Positive' appears by trained modes with labeling data and 'True Positive' data in monitoring of deep learning-based CCTV accident detection system, which is under operation at a tunnel monitoring center. Those types of 'False Positive' to 'fire' or 'person' objects were frequently taking place for lights of working vehicle, reflecting sunlight at tunnel entrance, long black feature which occurs to the part of lane or car, etc. To solve this problem, a deep learning model was developed by simultaneously training the 'False Positive' data generated in the field and the labeling data. As a result, in comparison with the model that was trained only by the existing labeling data, the re-inference performance with respect to the labeling data was improved. In addition, re-inference of the 'False Positive' data shows that the number of 'False Positive' for the persons were more reduced in case of training model including many 'False Positive' data. By training of the 'False Positive' data, the capability of field application of the deep learning model was improved automatically.